Distribution network reconstruction method, device and terminal for distributed wind power grid connection point voltage exceeding limit

Abstract

The present application relates to a method, device and terminal for reconfiguring a distribution network when the voltage at a distributed wind power grid connection point exceeds the limit, and belongs to the technical field of distributed wind power grid connection. In each iterative processing of the distribution network reconstruction when the voltage at a distributed wind power grid connection point exceeds the limit in the present application, the inertia weight of the particle group velocity update formula at the current iteration is obtained based on the current fitness value of the particle; and the learning factor of the particle group velocity update formula at the current iteration is determined based on the number of iterations at the current iteration, which can realize dynamic adjustment of the inertia weight and learning factor of the particle group velocity update formula during the search and optimization process, and help to realize that the distribution network reconstruction when the voltage at the distributed wind power grid connection point exceeds the limit has balanced global and local optimization capabilities, thereby improving the quality of the distribution network reconstruction when the voltage at the distributed wind power grid connection point exceeds the limit.

Description

Technical Field

[0001] The present application belongs to the technical field of distributed wind power grid connection, and specifically relates to a distribution network reconstruction method, device and terminal when the voltage at a distributed wind power grid connection point exceeds a limit. Background Art

[0002] At present, the wind power industry has developed rapidly. For the development of centralized wind power, some problems have gradually emerged, such as the high wind abandonment rate and serious wind power consumption problems, which have led to a bottleneck in the development of wind power. Therefore, decentralized wind power has become a new way to develop wind power.

[0003] The access of distributed wind power has changed the traditional distribution network mode of unidirectional flow. There is a possibility of reverse flow, which may cause the voltage at the grid connection point of the wind power generation system to exceed the limit. Voltage exceeding the limit not only affects the power quality of the local load, but also limits the penetration rate of wind power in the distribution network.

[0004] In the related technology, the particle swarm optimization algorithm is used to reconfigure the distribution network. During the search and optimization process, the inertia weight and the learning factor are fixed, which can easily lead to the distribution network reconstruction when the voltage at the distributed wind power grid connection point exceeds the limit and falls into the local optimum. Summary of the invention

[0005] To this end, the present application provides a distribution network reconstruction method, device and terminal for distributed wind power grid connection point voltage exceeding the limit, which helps to avoid the distribution network reconstruction for distributed wind power grid connection point voltage exceeding the limit falling into local optimum.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for reconfiguring a distribution network when voltage exceeds a limit at a distributed wind power grid connection point, the method comprising:

[0008] Obtaining a particle swarm of a distribution network, wherein particles in the particle swarm represent node switches, positions of particles indicate states of node switches, and speeds of particles indicate probabilities of specific node switch states;

[0009] Iterative processing is performed based on the positions and velocities of the particles in the particle swarm until a preset iteration termination condition is met to obtain a particle target position, and the distribution network is reconstructed using the obtained particle target position;

[0010] The processing steps for each iteration are as follows:

[0011] Based on the current position of the particle, determine the individual optimal position and the global optimal position of the particle swarm velocity update formula at the current iteration; determine the current fitness value of the particle, and based on the current fitness value of the particle, obtain the inertia weight of the particle swarm velocity update formula at the current iteration; and based on the number of iterations, determine the learning factor of the particle swarm velocity update formula at the current iteration;

[0012] The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration.

[0013] In a second aspect, the present application provides a distributed wind power grid connection point voltage over-limit distribution network reconstruction device, the device comprising:

[0014] An acquisition module is used to obtain a particle swarm of a distribution network, wherein particles in the particle swarm represent node switches, positions of particles indicate states of node switches, and speeds of particles indicate probabilities of specific node switch states;

[0015] A reconstruction processing module, used for performing iterative processing based on the position and speed of the particles in the particle swarm until a preset iteration termination condition is met to obtain the particle target position, and reconstructing the distribution network using the obtained particle target position;

[0016] The processing steps for each iteration are as follows:

[0017] Based on the current position of the particle, determine the individual optimal position and the global optimal position of the particle swarm velocity update formula at the current iteration; determine the current fitness value of the particle, and based on the current fitness value of the particle, obtain the inertia weight of the particle swarm velocity update formula at the current iteration; and based on the number of iterations, determine the learning factor of the particle swarm velocity update formula at the current iteration;

[0018] The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration.

[0019] In a third aspect, the present application provides a distribution network reconstruction processing terminal, including:

[0020] a memory having an executable program stored therein;

[0021] A processor is used to execute the executable program in the memory to implement the steps of any one of the above methods.

[0022] This application adopts the above technical solution, which has at least the following beneficial effects:

[0023] In each iterative processing process of the distribution network reconstruction when the voltage at the distributed wind power grid connection point exceeds the limit in the present application, the inertia weight of the particle swarm velocity update formula at the current iteration is obtained based on the current fitness value of the particle; and the learning factor of the particle swarm velocity update formula at the current iteration is determined based on the number of iterations at the current iteration, so that the inertia weight and learning factor of the particle swarm velocity update formula can be dynamically adjusted during the search and optimization process, which helps to achieve a balanced global and local optimization capability for the distribution network reconstruction when the voltage at the distributed wind power grid connection point exceeds the limit, thereby improving the quality of the distribution network reconstruction when the voltage at the distributed wind power grid connection point exceeds the limit.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a flow chart of a method for reconfiguring a distribution network when voltage exceeds a limit at a distributed wind power grid connection point according to an exemplary embodiment;

[0027] Figure 2 It is the schematic diagram of IEEE33 node system;

[0028] Figure 3 A schematic diagram to remove switches that are not in any loop and switches directly connected to the wind turbine;

[0029] Figure 4 A schematic diagram of merging branches belonging to nodes with a degree of 2 into one branch to form a branch group;

[0030] Figure 5 It is a schematic diagram of parallel processing of the branches that can be connected in parallel in the network.

[0031] Figure 6 It is a block diagram of a distribution network reconstruction device for a distributed wind power grid connection point voltage exceeding a limit according to an exemplary embodiment;

[0032] Figure 7 It is a block diagram of a distribution network reconstruction processing terminal according to an exemplary embodiment. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other implementation methods obtained by ordinary technicians in the field without creative work belong to the scope of protection of the present application.

[0034] See also Figure 1 , Figure 1 The present invention is a flow chart of a method for reconfiguring a distribution network when the voltage at a distributed wind power grid connection point exceeds a limit according to an exemplary embodiment. The method comprises the following steps:

[0035] Step S11, obtaining a particle swarm of the distribution network, wherein the particles in the particle swarm represent node switches, the positions of the particles indicate the states of the node switches, and the speeds of the particles indicate the probabilities of the states of the specific node switches.

[0036] Specifically, the particle's position x ih Each dimension of can be represented by a binary variable 0 or 1, 0 indicates open, 1 indicates closed. The velocity of the particle v ih Indicates the probability of the particle position taking 0 or 1. The greater the speed, the greater the probability of taking 1, and vice versa.

[0037] Step S12, performing iterative processing based on the position and speed of the particles in the particle swarm until a preset iteration termination condition is met, obtaining the particle target position, and reconstructing the distribution network using the obtained particle target position;

[0038] The processing steps for each iteration are as follows:

[0039] Based on the current position of the particle, determine the individual optimal position and the global optimal position of the particle swarm velocity update formula at the current iteration; determine the current fitness value of the particle, and based on the current fitness value of the particle, obtain the inertia weight of the particle swarm velocity update formula at the current iteration; and based on the number of iterations, determine the learning factor of the particle swarm velocity update formula at the current iteration;

[0040] The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration.

[0041] Specifically, in the particle swarm algorithm, the speed update formula of the particle swarm is as follows:

[0042]

[0043] Where ω is the inertia weight; represents the particle position; represents the particle speed; q1 and q2 are random factors, which can be random numbers in [0,1] in specific applications; c1 and c2 are learning factors, is the individual optimal position of particle i, is the global optimal position of the particle swarm. k is the number of iterations, and h is the dimension.

[0044] The particle swarm algorithm simulates birds in a flock by designing a massless particle. The particle has only two properties: speed and position. Each particle searches for the optimal solution in the search space individually and records it as the current individual extreme value. The individual extreme value is shared with other particles in the entire particle swarm. The best individual extreme value is found as the current global optimal solution of the entire particle swarm. All particles in the particle swarm adjust their speed and position according to the current individual extreme value they have found and the current global optimal solution shared by the entire particle swarm.

[0045] The inertia weight ω, learning factors c1 and c2 of the speed update formula of the above particle group are generally fixed settings, which easily lead to the distribution network reconstruction of the distributed wind power grid connection point voltage exceeding the limit falling into the local optimum. In the present application scheme, in each iterative processing process, based on the current fitness value of the particle, the inertia weight of the particle group speed update formula at the current iteration is obtained; and based on the number of iterations at the current iteration, the learning factor of the particle group speed update formula at the current iteration is determined, which can realize the dynamic adjustment of the inertia weight and learning factor of the particle group speed update formula during the search and optimization process, which is helpful to realize the distribution network reconstruction of the distributed wind power grid connection point voltage exceeding the limit with balanced global and local optimization capabilities, thereby improving the quality of the distribution network reconstruction of the distributed wind power grid connection point voltage exceeding the limit.

[0046] In one embodiment, the inertia weight of the particle swarm velocity update formula at the current iteration is obtained based on the current fitness value of the particle, including:

[0047] Input the particle's current fitness value into the following inertia weight update formula:

[0048]

[0049] Get the inertia weight of the particle swarm velocity update formula at the current iteration;

[0050] In the formula, ω i is the inertia weight of the i-th particle; ω max The maximum value of the inertia weight; F i is the current fitness value of the i-th particle; is the average fitness value of the particle swarm; γ is the preset inertia factor, which can range from [0,1];

[0051] in,

[0052] In this specific embodiment, the size of ω can be dynamically adjusted according to actual problems, and the particles can have balanced global and local search capabilities.

[0053] In one embodiment, the determining of the learning factor of the particle swarm velocity update formula at the current iteration based on the current iteration number includes:

[0054] Use the following formula group:

[0055]

[0056] Determine the learning factor of the particle swarm velocity update formula at the current iteration;

[0057] In the formula, c1 and c2 are two learning factors of the particle swarm velocity update formula, c 1max and c 1min is the upper and lower limits of c1, c 2max and c 2min is the upper and lower limits of c2, r is the number of iterations, r max The maximum number of iterations to set.

[0058] In this specific embodiment, the learning factor is re-determined in each iterative process to achieve dynamic adjustment of the learning factor and improve the optimization capability of the distributed wind power grid connection point voltage over-limit distribution network reconstruction.

[0059] For the step of using the particle velocity obtained by the particle swarm velocity update formula at the current iteration to obtain the particle position after the current iteration, the position update formula in the particle swarm algorithm related technology can be used. However, for the specific application scenario of the distributed wind power distribution network, the distribution network needs to meet the requirements of the radial structure during normal operation, and "island" and ring network phenomena cannot appear. Therefore, it is required that only one switch can be opened in each loop, that is, the number of switches opened must be equal to the number of loops. If the position of the particle is updated according to the previous position update formula, there will be a situation where more than one switch is opened in a loop, which will greatly increase the probability of infeasible switch combination states appearing in the distribution network reconstruction, seriously affecting the search efficiency of the algorithm. In order to overcome the above problems, the present application provides the following solutions:

[0060] In one embodiment, the particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration, including:

[0061] Use the following particle position update formula:

[0062]

[0063] Get the particle position after the iteration;

[0064] In the formula, q is a random number on [0,1]; is the particle velocity obtained by using the particle swarm velocity update formula at the current iteration; q ih is the minimum value obtained by the min function; S is the set of all switches that belong to a ring network with the node switch; j is the set of all particle velocities of the i-th particle in the h-th dimension at the k-th iteration, belongs to a number in set j; S is the velocity set of all particles in a ring network in the hth dimension. The min function uses Determine a minimum value q ih , and its corresponding particle position is determined to be 0.

[0065] In one embodiment, determining the current fitness value of the particle includes:

[0066] Based on the current position of the particle, the current fitness value of the particle is obtained by using the network loss and voltage deviation, based on the current fitness value of the particle.

[0067] Specifically, most of the existing reconstruction studies are aimed at improving the single operating characteristics of the distribution network, which may lead to a significant improvement in one operating characteristic but worsen other operating characteristics. In particular, there are fewer studies on multi-objective reconstruction that consider factors such as distributed wind power generation output. This application constructs a fitness function based on two objective functions: network loss and voltage offset. In practical applications, the relationship between the two is comprehensively considered, and weights are assigned to each objective function. The fitness function is composed of a multi-objective function, so the fitness function of the particle can be expressed as:

[0068] F = μ*f1+γ*f2;

[0069] In the formula, μ and γ are weight factors of the function; f1 and f2 are network loss index and voltage deviation index respectively. The network loss index and voltage deviation index will be further explained below.

[0070] The smaller the output value of the fitness function, the better the corresponding particle is and the closer it is to the target requirement.

[0071] In one embodiment, the iteration termination condition includes:

[0072] The number of iterations reaches the set number or the fitness statistics of the particle swarm after iteration meets the preset convergence condition; and

[0073] The particle swarm satisfies the preset constraints.

[0074] Specifically, for the set number of iterations, a set number can be obtained through a preliminary test simulation, and after the set number of iterations is completed, the output result can meet the reconstruction requirements. Alternatively, an iteration convergence condition can be set, such as when the absolute value of the difference between the fitness statistics of the iterative particle swarm and the target value is less than a certain set value, it is judged that the convergence condition is met.

[0075] Furthermore, the constraints include:

[0076] a. Power flow constraints:

[0077]

[0078] Where n is the total number of system nodes; P WTGi and Q WTGi are the active and reactive outputs of the wind turbine at node i respectively; P i and Q i are the active and reactive power input to node i respectively; Y is the admittance matrix of the branch; U i and U j are the voltages of nodes i and j respectively;

[0079] b. Node voltage constraints: U imin ≤U i ≤U imax ,

[0080] Where U i is the voltage at node i, U imin is the lower voltage limit of node i, U imax is the upper voltage limit of node i;

[0081] c. Distributed wind power penetration rate constraints:

[0082]

[0083] Where P WTGi and Q WTGi are the active and reactive outputs of the wind turbine at node i respectively; P WTGmin and Q WTGmin are the lower limits of active and reactive output of the wind turbine at node i; P WTGmax and Q WTGmax are the upper limits of active and reactive output of the wind turbine at node i respectively; the total power of the wind turbine shall not be greater than the penetration rate;

[0084] d. Network topology constraints: These constraints must be met during and after the distribution network reconstruction: the distribution point network structure must be radial and there must be no isolated islands or ring networks.

[0085] When the distribution network is reconstructed, since the particle position has a randomness of 0 or 1 in each dimension, a large number of invalid solutions that do not meet the network structure constraints will be generated. In this regard, the present application solves the above problem through the following embodiments:

[0086] In one embodiment, the method further comprises:

[0087] Before obtaining the particle swarm of the distribution network, the distribution network is simplified and reconstructed and encoded;

[0088] The simplification of the distribution network includes:

[0089] A. Close the switches that are not in any loop and the switches directly connected to the wind turbine; that is, the particle positions corresponding to these node switches in actual applications are always kept at 1, so that these switches are not encoded. Because if any of their switch positions is 0, an island will appear.

[0090] B. Merge the branches of nodes with a degree of 2 into one branch to form a branch group and number them;

[0091] C. Parallel processing of parallel-connectable branches;

[0092] See also Figure 2-5 ,The following takes the IEEE 33 node system as an example to illustrate the process of ,network structure simplification. Figure 2 It is an IEEE33-node system. In the figure, WTG1, WTG2, and WTG3 represent wind turbines respectively; Figure 3 To remove switches that are not in any loop and switches directly connected to wind turbines, nodes 0 to 1 and three switches directly connected to wind turbines are simplified; Figure 4 In order to merge the branches of nodes with a degree of 2 into one branch to form a branch group, the network is simplified from the original 33 nodes to 12 nodes; Figure 4 The branches that can be connected in parallel in the network are processed in parallel. Figure 3 The branches L1 and L2 in the circuit are connected in parallel to form one branch. Figure 4 The network shown.

[0093] The reconstruction coding process of the distribution network includes:

[0094] Each loop is set to open only one node switch, and the node switch is coded based on the loop.

[0095] Specifically, in order to ensure that the power grid is always radial and reduce the probability of infeasible solutions, the present application adopts a loop-based coding method. Each interconnecting switch corresponds to a ring network, and each loop can only open one switch. The corresponding particle coding is that for each ring network, only one-dimensional position is 0, and the rest are 1.

[0096] By simplifying and reconstructing the encoding of the distribution network, the number of nodes can be reduced by simplifying the network structure, the particle encoding dimension can be reduced, the solution space can be compressed, and the solution speed can be accelerated.

[0097] In the related art, the particle swarm algorithm pursues fast convergence speed and high search efficiency, but often at the cost of lack of diversity, thus having an adverse impact on the final optimization result. In this regard, the present application provides the following embodiment to overcome this problem.

[0098] In one embodiment, the method further comprises:

[0099] Before each iterative process, for the particle group, the particles are arranged in ascending order of fitness values;

[0100] Based on the fitness values ​​of the sequentially arranged particles, the jumping degrees of the particles are calculated, and when it is determined that the jumping degree of the next particle is less than the jumping degrees of all the previous particles, all the previous particles are divided into a subgroup;

[0101] For the remaining particles arranged in sequence, return to the execution step: based on the fitness value of the particles arranged in sequence, calculate the jump degree of the particles; when it is determined that the jump degree of the next particle is less than the jump degree of all previous particles, divide all the previous particles into a subgroup; until the subgroup division is completed, iterative processing is performed on each subgroup separately.

[0102] Through the above scheme, the particle swarm can be divided into multiple subgroups, and each subgroup does not cross each other. Before each iterative process, the particle fitness value is used to obtain the particle jump degree, so as to re-divide the particle swarm into subgroups. Since the particle fitness value changes after each iterative process, the subgroups change dynamically between iterations during the continuous iterative process, which helps to expand the breadth and depth in the search process, ensure diversity through further divided subgroups, and improve the optimization results of distribution network reconstruction.

[0103] For the above scheme, specifically, for the particle swarm, the fitness of each particle is calculated, and the fitness value of the particle is defined as the feature point to obtain the feature set L. The feature set L is sorted from small to large according to the fitness value F, and the jump degree of each particle is calculated:

[0104]

[0105] in,

[0106]

[0107] Where n is the total number of particles, F kis the fitness value of the kth particle.

[0108] In this scheme, based on the jump degree j k , with F k The feature stratification is performed as the boundary. Only when the jump degree j of the kth particle k Less than the jump degree of the previous k-1 feature points, F k Each time the stratification is performed, the particles that have been stratified are removed, and the remaining particles are stratified again until all the features are stratified, so as to obtain multiple subgroups.

[0109] In order to avoid unnecessary operations and waste of resources, when the power grid is actually running, it is not necessary to perform reconstruction operations in any network state, but it is necessary to consider whether the current network needs to be reconstructed, that is, to optimize the starting conditions of reconstruction. Therefore, it is necessary to evaluate the relevant indicators of the current network to determine whether reconstruction is needed. This application provides the following implementation scheme:

[0110] In one embodiment, the method further comprises:

[0111] The network loss and voltage quality of the distributed wind power distribution network are obtained, and when any one of the network loss and the voltage quality exceeds a limit, an execution step is determined to perform iterative processing based on the position and speed of particles in the particle group, and subsequent steps.

[0112] This application selects network loss and voltage quality to evaluate the current network, and sets the threshold of each indicator. The indicator is calculated and compared with the corresponding threshold to determine whether it exceeds the limit.

[0113] Network loss:

[0114]

[0115] Where N is the total number of branches; i is the branch number; K i is the switch state of branch number i, 0 and 1 are switch open and closed respectively; R i Indicates the resistance value of branch i; I i is the load current flowing through branch i.

[0116] Voltage quality:

[0117] In order to control the voltage quality of the node, the evaluation function of the voltage quality is defined as the voltage deviation of the system. The mathematical expression is described as follows:

[0118]

[0119] Where ΔU represents the voltage deviation, N represents the total number of distribution network nodes; U irepresents the actual voltage of node i; U iN Represents the rated voltage of node i.

[0120] According to the formula provided above, the network loss and voltage deviation are obtained through power flow calculation. When any indicator exceeds the limit, reconstruction calculation is implemented.

[0121] See also Figure 6 , Figure 6 1 is a block diagram of a distributed wind power grid connection point voltage over-limit distribution network reconstruction device according to an exemplary embodiment, the distribution network reconstruction device 6 includes:

[0122] An acquisition module 61 is used to obtain a particle swarm of a distribution network, wherein particles in the particle swarm represent node switches, positions of particles indicate states of node switches, and speeds of particles indicate probabilities of specific node switch states;

[0123] A reconstruction processing module 62 is used to perform iterative processing based on the position and speed of the particles in the particle swarm until a preset iteration termination condition is met to obtain the particle target position, and to reconstruct the distribution network using the obtained particle target position;

[0124] The processing steps for each iteration are as follows:

[0125] Based on the current position of the particle, determine the individual optimal position and the global optimal position of the particle swarm velocity update formula at the current iteration; determine the current fitness value of the particle, and based on the current fitness value of the particle, obtain the inertia weight of the particle swarm velocity update formula at the current iteration; and based on the number of iterations, determine the learning factor of the particle swarm velocity update formula at the current iteration;

[0126] The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration.

[0127] Furthermore, in the reconstruction processing module 62, the inertia weight of the particle group velocity update formula at the current iteration is obtained based on the current fitness value of the particle, including:

[0128] Input the particle's current fitness value into the following inertia weight update formula:

[0129]

[0130] Get the inertia weight of the particle swarm velocity update formula at the current iteration;

[0131] In the formula, ω i is the inertia weight of the i-th particle; ω max The maximum value of the inertia weight; F i is the current fitness value of the i-th particle; is the average fitness value of the particle swarm; γ is the preset inertia factor.

[0132] Furthermore, in the reconstruction processing module 62, the learning factor of the particle swarm velocity update formula at the current iteration is determined based on the number of iterations, including:

[0133] Use the following formula group:

[0134]

[0135] Determine the learning factor of the particle swarm velocity update formula at the current iteration;

[0136] Where c1 and c2 are two learning factors of the particle swarm velocity update formula; c 1max and c 1min is the upper and lower limits of c1; c 2max and c 2min are the upper and lower limits of c2; r is the number of iterations; r max The maximum number of iterations to set.

[0137] Furthermore, in the reconstruction processing module 62, the particle velocity obtained by using the particle group velocity update formula at the current iteration to obtain the particle position after the current iteration includes:

[0138] Use the following particle position update formula:

[0139]

[0140] Get the particle position after the iteration;

[0141] In the formula, q is a random number on [0,1]; is the particle velocity obtained by using the particle swarm velocity update formula at the current iteration; q ih is the minimum value obtained by the min function; S is the set of all switches that belong to a ring network with the node switch; j is the set of all particle velocities of the i-th particle in the h-th dimension at the k-th iteration, A number belonging to the set j; S is the set of velocities of all particles belonging to a ring network in the hth dimension.

[0142] Furthermore, in the reconstruction processing module 62, the determining of the current fitness value of the particle includes:

[0143] Based on the current position of the particle, the current fitness value of the particle is obtained by using the network loss and voltage deviation, based on the current fitness value of the particle.

[0144] Furthermore, in the reconstruction processing module 62, the iteration termination condition includes:

[0145] The number of iterations reaches the set number or the fitness statistics of the particle swarm after iteration meets the preset convergence condition; and

[0146] The particle swarm satisfies the preset constraints.

[0147] Furthermore, in the reconstruction processing module 62, the constraints include:

[0148] a. Power flow constraints:

[0149]

[0150] Where n is the total number of system nodes; P WTGi and Q WTGi are the active and reactive outputs of the wind turbine at node i respectively; P i and Q i are the active and reactive power input to node i respectively; Y is the admittance matrix of the branch; U i and U j are the voltages of nodes i and j respectively;

[0151] b. Node voltage constraints: U imin ≤U i ≤U imax ,

[0152] Where U i is the voltage at node i, U imin is the lower voltage limit of node i, U imax is the upper voltage limit of node i;

[0153] c. Distributed wind power penetration rate constraints:

[0154]

[0155] Where P WTGi and Q WTGi are the active and reactive outputs of the wind turbine at node i respectively; P WTGmin and Q WTGmin are the lower limits of active and reactive output of the wind turbine at node i; P WTGmax and Q WTGmax are the upper limits of active and reactive output of the wind turbine at node i respectively; the total power of the wind turbine shall not be greater than the penetration rate;

[0156] d. Network topology constraints: These constraints must be met during and after the distribution network reconstruction: the distribution point network structure must be radial and there must be no isolated islands or ring networks.

[0157] Furthermore, the distribution network reconstruction device 6 also includes:

[0158] A simplification processing and reconstruction coding processing module is used to perform simplification processing and reconstruction coding processing on the distribution network before obtaining the particle swarm of the distribution network;

[0159] The simplification of the distribution network includes:

[0160] A. Close the switches that are not in any loop and the switches directly connected to the wind turbine;

[0161] B. Merge the branches of nodes with a degree of 2 into one branch to form a branch group and number them;

[0162] C. Parallel processing of parallel-connectable branches;

[0163] The reconstruction coding process of the distribution network includes:

[0164] Each loop is set to open only one node switch, and the node switch is coded based on the loop.

[0165] Furthermore, the distribution network reconstruction device 6 also includes:

[0166] A subgroup division module is used to arrange the particles in the particle group in ascending order of fitness values ​​before each iterative processing;

[0167] Based on the fitness values ​​of the sequentially arranged particles, the jumping degrees of the particles are calculated, and when it is determined that the jumping degree of the next particle is less than the jumping degrees of all the previous particles, all the previous particles are divided into a subgroup;

[0168] For the remaining particles arranged in sequence, return to the execution step: based on the fitness value of the particles arranged in sequence, calculate the jump degree of the particles; when it is determined that the jump degree of the next particle is less than the jump degree of all previous particles, divide all the previous particles into a subgroup; until the subgroup division is completed, iterative processing is performed on each subgroup separately.

[0169] Furthermore, the distribution network reconstruction device 6 also includes:

[0170] The indicator over-limit determination module is used to obtain the network loss and voltage quality of the distributed wind power distribution network, and when any one of the network loss and the voltage quality indicators exceeds the limit, determine the execution step to iterate based on the position and speed of the particles in the particle group, and its subsequent steps.

[0171] Regarding the distribution network reconstruction device 6 in the above embodiment, the specific manner in which each module thereof performs operations has been described in detail in the above embodiment of the related method, and will not be elaborated on here.

[0172] See also Figure 7 , Figure 7 is a block diagram of a power distribution network reconstruction processing terminal according to an exemplary embodiment. The power distribution network reconstruction processing terminal 7 includes:

[0173] A memory 71 on which an executable program is stored;

[0174] The processor 72 is used to execute the executable program in the memory 71 to implement the steps of any one of the above methods.

[0175] In practical applications, the power distribution network reconstruction processing terminal 7 may be a computer. Specifically, for the power distribution network reconstruction processing terminal 7, the specific manner in which the processor 72 executes the program in the memory 71 has been described in detail in the embodiment of the method, and will not be elaborated here.

[0176] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute any of the above methods. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD), or a solid-state drive (SSD); the storage medium may also include a combination of the above-mentioned types of memory.

[0177] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0178] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" and "multiple" refers to at least two.

[0179] It should be understood that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or there may be an intermediate element at the same time; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intermediate element at the same time. In addition, the "connection" used here may include wireless connection; the wording "and / or" used includes any unit and all combinations of one or more associated listed items.

[0180] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0181] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0182] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0183] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0184] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0185] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0186] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for reconfiguring a distributed wind power distribution network when the voltage at the grid connection point exceeds the limit, characterized in that: The method comprises: Obtaining a particle swarm of a distribution network, wherein particles in the particle swarm represent node switches, positions of particles indicate states of node switches, and speeds of particles indicate probabilities of specific node switch states; Iterative processing is performed based on the positions and velocities of the particles in the particle swarm until a preset iteration termination condition is met to obtain a particle target position, and the distribution network is reconstructed using the obtained particle target position; The processing steps for each iteration are as follows: Based on the current position of the particle, determine the individual optimal position and the global optimal position of the particle swarm velocity update formula at the current iteration; determine the current fitness value of the particle, and based on the current fitness value of the particle, obtain the inertia weight of the particle swarm velocity update formula at the current iteration; and based on the number of iterations, determine the learning factor of the particle swarm velocity update formula at the current iteration; The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration. The inertia weight of the particle swarm velocity update formula at the current iteration is obtained based on the current fitness value of the particle, including: Input the particle's current fitness value into the following inertia weight update formula: ， Get the inertia weight of the particle swarm velocity update formula at the current iteration; In the formula, For the The inertia weight of each particle; Set the maximum value for the inertia weight; For the The current fitness value of each particle; is the average fitness value of the particle swarm; is the preset inertia factor; The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration, including: Use the following particle position update formula: ， Get the particle position after the iteration; In the formula, is a random number on [0,1]; The particle velocity is obtained by using the particle group velocity update formula at the current iteration; The minimum value obtained by the min function; is the set of all switches that belong to a ring network with the node switch; For the The particle in The iteration is at The set of all particle velocities in the dimension, Belongs to the collection A number in For the The velocity set of all particles belonging to a ring network.

2. The method according to claim 1, characterized in that The method of determining the learning factor of the particle swarm velocity update formula at the current iteration based on the current iteration number includes: Use the following formula group: ， Determine the learning factor of the particle swarm velocity update formula at the current iteration; In the formula, and Two learning factors for the particle swarm velocity update formula; and for The upper and lower limits of and for The upper and lower limits of is the number of iterations; The maximum number of iterations to set.

3. The method according to claim 1, characterized in that Determining the current fitness value of the particle includes: Based on the current position of the particle, the current fitness value of the particle is obtained using network loss and voltage deviation.

4. The method according to claim 1, characterized in that: The iteration termination conditions include: The number of iterations reaches the set number or the fitness statistics of the particle swarm after iteration meets the preset convergence condition; and The particle swarm satisfies the preset constraints.

5. The method according to claim 4, characterized in that The constraints include: a. Power flow constraints: ， In the formula, is the total number of system nodes; and Node Active and reactive output of the fan; and Node The input active and reactive power; is the admittance matrix of the branch; and Node , Voltage; b. Node voltage constraints: , In the formula, For Node Voltage, For Node Voltage lower limit, For Node Voltage upper limit; c. Distributed wind power penetration rate constraints: ， In the formula, and Node Active and reactive output of the fan; and Node The lower limit of active and reactive output of the fan; and Node The upper limit of active and reactive output of the fan shall be set; the total power of the fan shall not be greater than the penetration rate; d. Network topology constraints: These constraints must be met during and after the distribution network reconstruction: the distribution point network structure must be radial and there must be no isolated islands or ring networks.

6. The method according to claim 4, characterized in that The method further comprises: Before obtaining the particle swarm of the distribution network, the distribution network is simplified and reconstructed and encoded; The simplification of the distribution network includes: A. Close the switches that are not in any loop and the switches directly connected to the wind turbine; B. Merge the branches of nodes with a degree of 2 into one branch to form a branch group and number them; C. Parallel processing of parallel-connectable branches; The reconstruction coding process of the distribution network includes: Each loop is set to open only one node switch, and the node switch is coded based on the loop.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Before each iterative process, for the particle group, the particles are arranged in ascending order of fitness values; Based on the fitness values ​​of the sequentially arranged particles, the jumping degrees of the particles are calculated, and when it is determined that the jumping degree of the next particle is less than the jumping degrees of all the previous particles, all the previous particles are divided into a subgroup; For the remaining particles arranged in sequence, return to the execution step: based on the fitness values ​​of the particles arranged in sequence, calculate the jump degree of the particles, and when it is determined that the jump degree of the next particle is less than the jump degrees of all the previous particles, divide all the previous particles into a subgroup; until the subgroup division is completed, iterative processing is performed on each subgroup respectively; The jump degree is calculated by the following formula: ； The above formula, For the The jumping speed of each particle; ； In the above formula, is the total number of particles, For the The fitness value of a particle.

8. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: The network loss and voltage quality of the distributed wind power distribution network are obtained, and when any one of the network loss and the voltage quality exceeds a limit, an execution step is determined to perform iterative processing based on the position and speed of particles in the particle group, and subsequent steps.

9. A distributed wind power grid connection point voltage over-limit distribution network reconstruction device, characterized in that: The device comprises: An acquisition module is used to obtain a particle swarm of a distribution network, wherein particles in the particle swarm represent node switches, positions of particles indicate states of node switches, and speeds of particles indicate probabilities of specific node switch states; A reconstruction processing module, used for performing iterative processing based on the position and speed of the particles in the particle swarm until a preset iteration termination condition is met to obtain the particle target position, and reconstructing the distribution network using the obtained particle target position; The processing steps for each iteration are as follows: Based on the current position of the particle, determine the individual optimal position and the global optimal position of the particle swarm velocity update formula at the current iteration; determine the current fitness value of the particle, and based on the current fitness value of the particle, obtain the inertia weight of the particle swarm velocity update formula at the current iteration; and based on the number of iterations, determine the learning factor of the particle swarm velocity update formula at the current iteration; The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration. The inertia weight of the particle swarm velocity update formula at the current iteration is obtained based on the current fitness value of the particle, including: Input the particle's current fitness value into the following inertia weight update formula: ， Get the inertia weight of the particle swarm velocity update formula at the current iteration; In the formula, For the The inertia weight of each particle; Set the maximum value for the inertia weight; For the The current fitness value of each particle; is the average fitness value of the particle swarm; is the preset inertia factor; The particle position after the iteration is obtained by using the particle velocity obtained by the particle group velocity update formula at the iteration, including: Use the following particle position update formula: ， Get the particle position after the iteration; In the formula, is a random number on [0,1]; The particle velocity is obtained by using the particle group velocity update formula at the current iteration; The minimum value obtained by the min function; is the set of all switches that belong to a ring network with the node switch; For the The particle in The iteration is at The set of all particle velocities in the dimension, Belongs to the collection A number in For the The velocity set of all particles belonging to a ring network.

10. A distribution network reconstruction processing terminal, characterized in that: include: a memory having an executable program stored therein; A processor, configured to execute the executable program in the memory to implement the steps of the method according to any one of claims 1 to 8.

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